A Probabilistic Approach in Historical Linguistics Word Order Change in Infinitival Clauses: from Latin to Old French

TL;DR

This paper introduces a probabilistic, interdisciplinary method combining computational linguistics, Bayesian statistics, and sociolinguistics to analyze the historical change in word order in Latin and Old French infinitival clauses.

Contribution

It presents a novel three-stage probabilistic model of word order change, integrating pragmatic, syntactic, and sociolinguistic factors with computational methods.

Findings

Identified pragmatic neutrality in discourse contexts during language change

Mapped changes in information structure and syntactic constraints over time

Proposed a method to distinguish stable from changing word order alternations

Abstract

This research offers a new interdisciplinary approach to the field of Linguistics by using Computational Linguistics, NLP, Bayesian Statistics and Sociolinguistics methods. This thesis investigates word order change in infinitival clauses from Object-Verb (OV) to Verb-Object (VO) in the history of Latin and Old French. By applying a variationist approach, I examine a synchronic word order variation in each stage of language change, from which I infer the character, periodization and constraints of diachronic variation. I also show that in discourse-configurational languages, such as Latin and Early Old French, it is possible to identify pragmatically neutral contexts by using information structure annotation. I further argue that by mapping pragmatic categories into a syntactic structure, we can detect how word order change unfolds. For this investigation, the data are extracted from annotated corpora spanning several centuries of Latin and Old French and from additional resources created by using computational linguistic methods. The data are then further codified for various pragmatic, semantic, syntactic and sociolinguistic factors. This study also evaluates previous factors proposed to account for word order alternation and change. I show how information structure and syntactic constraints change over time and propose a method that allows researchers to differentiate a stable word order alternation from alternation indicating a change. Finally, I present a three-stage probabilistic model of word order change, which also conforms to traditional language change patterns.